Functional genetic variations in splicing regulation

描述(申请人提供)：最近，在构建疾病基因组或跨种群的遗传变异目录方面取得了巨大的成功。下一个重大挑战是阐明各种遗传变异在生物和疾病过程中的潜在功能。一类重要的功能变异包括那些影响顺式基因表达的变异。事实上，顺式调控变异体参与了广泛的疾病，而且它们对基因表达的影响一直比反式作用决定因素更强。选择性剪接是顺式调控改变发生的重要机制。先前的研究估计，导致人类遗传病的点突变中有15%-60%会破坏剪接，这突显了这一调控步骤的重要性。此外 对于众所周知的剪接位点信号，剪接受到许多外显子或内含子的密切调控 与反式作用蛋白相关的元素。这些顺式调控元件的破坏可能导致异常剪接。然而，这一关键的监管方面在很大程度上仍未得到探索。我们建议结合计算、基因组和分子方法来研究遗传变异引起的剪接变化。其具体目标是：(1)通过高通量转录组测序(RNA-Seq)的生物信息学分析，全球识别受遗传变异的替代等位基因调控的差异剪接调控的外显子和基因。(2)利用微基因实验鉴定剪接改变的致病遗传变异。(3)利用机器学习方法、RNA-Seq数据和分子验证方法，建立了一个预测剪接改变原因遗传变异的综合模型。该项目将阐明剪接中顺式调控的功能性遗传变异，并为基因变异在人类疾病中的参与提供重要的见解。此外，这项工作将产生有价值的生物信息学工具，以充分利用各种细胞类型中日益可用的RNA-Seq数据来识别和预测与疾病相关的遗传变异。

DESCRIPTION (provided by applicant): Recently, tremendous success has been achieved in constructing a catalog of genetic variants in disease genomes or across population. The next great challenge is to elucidate the potential function of various genetic variants in biological an disease processes. An important type of functional variants consists of those that affect gene expression in cis. Indeed, cis-regulatory variants are involved in a broad range of diseases and they showed a consistently stronger influence on gene expression than trans-acting determinants. Alternative splicing is an essential mechanism via which cis-regulatory changes may occur. Previous studies estimated that 15- 60% of point mutations that result in human genetic diseases disrupt splicing, highlighting the importance of this regulatory step. In addition to the well-known splice site signals, splicing is closely regulated by many exonic or intronic cis elements, associated with trans-acting proteins. Disruption of these cis-regulatory elements can cause aberrant splicing. Yet this crucial regulatory aspect remains largely unexplored. We propose to combine computational, genomic and molecular approaches to study splicing changes due to genetic variations. The specific aims are: (1) To globally identify exons and genes that are under differential splicing regulation by the alternative alleles of genetic variant, via bioinformatic analysis of high-throughput sequencing of transcriptome profiles (RNA-Seq). (2) To identify causal genetic variants in splicing alteration using minigene-based experiments. (3) To develop an integrative model to predict causal genetic variants in splicing alteration, using machine learning approaches, RNA- Seq data and molecular validations. This project will elucidate functional cis-regulatory genetic variants in splicing and provide significant insight ino the involvement of genetic variations in human diseases. In addition, this work will generate valuable bioinformatic tools to make full use of the increasingly available RNA-Seq data in a wide variety of cell types for identification and prediction of disease-related genetic variants.